Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
Department of Psychiatry, Women's College Hospital, Toronto, Ontario, Canada.
J Clin Epidemiol. 2022 Jul;147:168-170. doi: 10.1016/j.jclinepi.2022.05.008. Epub 2022 May 27.
Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or "classes") within a heterogenous population. LCA identifies these hidden classes by a set of predefined features, known as "indicators". Unlike many other grouping analytical approaches, LCA derives classes using a probabilistic approach. In this first paper, we describe the common applications of LCA, and outline its advantages over other analytical subgrouping methods.
潜在类别分析（LCA）为对异质人群中的群体（或“类别”）进行分类提供了一种强大的分析方法。LCA 通过一组称为“指标”的预定义特征来识别这些隐藏的类别。与许多其他分组分析方法不同，LCA 使用概率方法来确定类别。在这第一篇论文中，我们描述了 LCA 的常见应用，并概述了它相对于其他分析亚组方法的优势。
